I am currently working on a problem and now got stuck to implement one of it's steps. This is a simple attempt to explain what I am currently facing, which is something that I am aiming to implement in my python simulation.

The idea is that I will some input parameters into my simulation, however one simulation is not able to perfectly capture all the dynamics involved in a real scenario. Hence, what I am aiming to do is to feed some inputs of the real scenario into my simulate and perform the simulation for all cases in which I have real data. So I will have the same amount of data for technically the same situation for both real and simulated scenario.

With my simulated data I can find out the optimal parameters(for the simulation), so the idea now is to correlate my simulated model with the real data, and then, with this correlation, find out what would be the equivalent of the optimal simulated parameters into the the optimal real parameters. Here not really precise diagram that might help on the visualization of the problem:

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I have already seen a lot of machine learning being utilized to fit to a set of data, but haven't really seen anything that could help me on this task that I currently have in hand, as "fitting models". So here comes the questions, how to correlate the models and utilized it to extract the optimal parameters.

Hope that I managed to be succinct and precise albeit the length of the text. I would really appreciate your help on this one!


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